TY - GEN
T1 - Automatic FDG-PET-based tumor and metastatic lymph node segmentation in cervical cancer
AU - Rodríguez Arbonès, Dídac
AU - Jensen, Henrik Grønholt
AU - Loft, Annika
AU - af Rosenschöld, Per Munck
AU - Hansen, Anders Elias
AU - Igel, Christian
AU - Darkner, Sune
PY - 2014
Y1 - 2014
N2 - Treatment of cervical cancer, one of the three most commonly diagnosed cancers worldwide, often relies on delineations of the tumour and metastases based on PET imaging using the contrast agent 18F-Fluorodeoxyglucose (FDG). We present a robust automatic algorithm for segmenting the gross tumour volume (GTV) and metastatic lymph nodes in such images. As the cervix is located next to the bladder and FDG is washed out through the urine, the PET-positive GTV and the bladder cannot be easily separated. Our processing pipeline starts with a histogram-based region of interest detection followed by level set segmentation. After that, morphological image operations combined with clustering, region growing, and nearest neighbour labelling allow to remove the bladder and to identify the tumour and metastatic lymph nodes. The proposed method was applied to 125 patients and no failure could be detected by visual inspection. We compared our segmentations with results from manual delineations of corresponding MR and CT images, showing that the detected GTV lays at least 97.5% within the MR/CT delineations. We conclude that the algorithm has a very high potential for substituting the tedious manual delineation of PET positive areas.
AB - Treatment of cervical cancer, one of the three most commonly diagnosed cancers worldwide, often relies on delineations of the tumour and metastases based on PET imaging using the contrast agent 18F-Fluorodeoxyglucose (FDG). We present a robust automatic algorithm for segmenting the gross tumour volume (GTV) and metastatic lymph nodes in such images. As the cervix is located next to the bladder and FDG is washed out through the urine, the PET-positive GTV and the bladder cannot be easily separated. Our processing pipeline starts with a histogram-based region of interest detection followed by level set segmentation. After that, morphological image operations combined with clustering, region growing, and nearest neighbour labelling allow to remove the bladder and to identify the tumour and metastatic lymph nodes. The proposed method was applied to 125 patients and no failure could be detected by visual inspection. We compared our segmentations with results from manual delineations of corresponding MR and CT images, showing that the detected GTV lays at least 97.5% within the MR/CT delineations. We conclude that the algorithm has a very high potential for substituting the tedious manual delineation of PET positive areas.
U2 - 10.1117/12.2042909
DO - 10.1117/12.2042909
M3 - Article in proceedings
T3 - Progress in Biomedical Optics and Imaging
BT - Medical Imaging 2014
A2 - Ourselin, Sebastian
A2 - Styner, Martin A.
PB - SPIE - International Society for Optical Engineering
T2 - Medical Imaging 2014
Y2 - 15 February 2014
ER -
